As image processing technology has advanced, so has the number of applications for the technology. In particular, image processing systems have proved extremely useful in moving object indication applications.
In the field of moving object indication, sequential images from a sensor can be analyzed to create an optical flow field. See, e.g., "Determining Optical Flow", B. K. P. Horn and B. G. Schunck, Computer Vision, p. 185, North-Holland Publishing, 1981. This optical flow field is an array of vectors indicating the magnitude and direction of movement on a pixel by pixel basis. The optical flow field can then be used to indicate motion or to track a moving object, for example, for purposes of targeting or security monitoring. Other uses of the optical flow field include sensor motion estimation, terrain structure estimation, and autonomous navigation.
Because of noise introduced in acquiring images, errors often exist in the optical flow vectors generated for a given object. For example, if the optical flow vectors corresponding to an object moving across the image plane of an image sensor should be equal at any given point in time, noise introduced by the image sensor could result in non-equal optical flow vectors corresponding to the object. To minimize these errors, smoothing operations are performed on the optical flow vectors. See, e.g., "Image Flow: Fundamentals and Algorithms", B. G. Schunck, Motion Understanding--Robot and Human Vision, Chapter 2, Kluwer Academic Publishers, 1988.
Present imaging systems rely on "guesses" made by a human operator to determine certain factors involved in minimizing errors in optical flow fields. Therefore, a need has arisen for a system that performs error minimization (smoothing) based upon the image, rather than on ad hoc determinations by the user of the system.